Diagnosis Is Not Prescription: Linguistic Co-Adaptation Explains Patching Hazards in LLM Pipelines
arXiv:2605.21958v1 Announce Type: new Abstract: When a multi-module LLM agent fails, the module most responsible for the failure is not necessarily the best place to intervene. We demonstrate this Diagnostic Paradox empirically: causal analysis consistently identifies the routing module -- which selects which tool to call next -- as the primary bottleneck across three independent agent families. Yet injecting prompt-level correction examples into this module consistently degrades performance, sometimes severely. Patching an upstream query-rewriting module instead reliably improves outcomes. Th
The rapid development and deployment of LLM agents make understanding their failure modes and proper debugging strategies critical for their effective evolution and widespread adoption.
This research provides a crucial insight into debugging and improving complex AI systems, highlighting that intuitive patching can be counterproductive and suggesting a need for more sophisticated diagnostic approaches in AI development.
The understanding of how to effectively patch and improve multi-module LLM agents shifts, moving from direct intervention at the point of failure to a more systemic, causal analysis approach.
- · AI researchers
- · LLM developers
- · AI agent platform providers
- · Inefficient AI debugging methodologies
- · LLM agent deployments reliant on naive patching
AI developers will adopt more sophisticated debugging tools and methodologies for LLM agents.
The reliability and performance of complex AI agents will improve faster, accelerating their integration into various industries.
Increased trust in AI agent performance could lead to broader adoption in critical applications, potentially driving new demand for AI infrastructure.
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Read at arXiv cs.CL